Tropical cyclone identification and tracking system using integrated neural oscillatory elastic graph matching and hybrid RBF network track mining techniques

In this paper, we present an automatic and integrated neural network-based tropical cyclone (TC) identification and track mining system. The proposed system consists of two main modules: 1) TC pattern identification system using neural oscillatory elastic graph matching model (NOEGM); and 2) TC track mining system using hybrid radial basis function (HRBF) network with time difference and structural learning (TDSL) algorithm. For system evaluation, 120 TC cases appeared in the period between 1985 and 1998 provided by National Oceanic and Atmospheric Administration (NOAA) are being used, In TC pattern recognition from satellite pictures, an overall 98% of correct TC pattern segmentation rate and over 97% of correct classification rate are attained. Moreover, for TC track and intensity mining test, promising result of over 86% is achieved with the application of the hybrid RBF network. Comparing with the bureau numerical TC prediction model (OTCM) used by Guam and the enhanced model (TFS) proposed by Jeng et al, [10], the proposed hybrid REP has attained an over 30% and 18% improvement in forecast errors.